Experience Requirements:

• 4–7 years of experience in Customer Success / Account Management at a B2B SaaS or fintech company.

• Have worked with technical products (APIs, data platforms, risk/fraud tooling a plus).

• Are comfortable leading executive conversations (Heads of Risk, Product, Payments).

• Have experience driving renewals, expansions, and customer advocacy.

• Thrive in an early-stage environment: scrappy, adaptable, execution-oriented.

Other Requirements:

• You’ve built or scaled a Customer Success function before.

Responsibilities:

• Own the entire customer lifecycle post-implementation

• Run account reviews (QBRs, regular touchpoints) to drive measurable outcomes.

• Proactively manage customer health: track adoption, identify risks, and address issues before they escalate.

• Drive renewals and expansions by spotting growth opportunities early.

• Be the voice of the customer and relay feedback to Product and Engineering.

Experience Requirements:

• 3+ years building production systems in Python/Django with Postgres.

• Hands-on experience fine-tuning and optimizing LLMs/SLMs, ideally in fraud, anomaly detection, or adversarial domains.

• A track record of reducing latency/cost in ML inference without compromising accuracy.

• Comfort working across the stack - from PyTorch profiling to Django APIs.

• An experimental but practical mindset: ship fast, measure rigorously, iterate.

Other Requirements:

• Prior work with imbalanced datasets (e.g., 1 in 10,000 fraud cases).

• Knowledge of feature stores, online learning, and temporal aggregation for fraud models.

• Familiarity with regulatory requirements around PII, KYC/AML, and compliance in financial data.

Responsibilities:

• Fine-tune, distill, and quantize LLMs and small language models (SLMs) for fraud detection tasks: entity resolution, anomaly detection, customer communication classification, synthetic data generation.

• Optimize inference so our models run fast and cost-efficiently in production - using techniques like lightweight fine-tuning (LoRA/PEFT), quantization to smaller precisions, and modern serving frameworks (e.g. vLLM, TensorRT)

• Build training/eval pipelines for fraud models that balance recall (catch fraud) with precision (minimize false positives).

• Create golden datasets, adversarial test sets, and online/offline evaluation harnesses that mirror real-world fraud evolution.

• Architect and own Python/Django services that integrate model predictions directly into customer-facing APIs.